Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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High-throughput single-cell and multi-omics measurements provide detailed views of cellular states, yet connecting these observations to the mechanisms governing tissue dynamics remains challenging. Our work combines experimental perturbations with computational analysis and mechanistic mathematical modelling to investigate how interactions between the host and its microbiota influence intestinal epithelial homeostasis.<>Using Tlr2-mediated microbial sensing as a biological example, we explore how changes in epithelial signalling and metabolism relate to stem cell differentiation, crypt composition and antimicrobial function. We integrate measurements across host and microbial compartments to identify candidate processes linking microbial cues to epithelial cell states.<>Alongside these analyses, we have established an ordinary differential equation (ODE)-based model of intestinal epithelial cell population dynamics. This provides a framework for examining how changes in proliferation, differentiation and turnover may contribute to observed differences in tissue composition. Our ongoing work investigates how molecular signatures can inform model assumptions and parameters, and how complementary modelling approaches can help formulate experimentally testable hypotheses.<>The talk will present our experimental and computational strategy, selected findings and current modelling developments. It will also discuss the opportunities and challenges of translating multi-omics measurements into mechanistic explanations of tissue organisation and its response to perturbation.

Referent:  Dr. Lorenz Adlung

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<>Dr. Lorenz Adlung leads the Adipose Systems Immunology group at the University Medical Center Hamburg-Eppendorf (UKE), where he has been an independent junior group leader since 2021. He is affiliated with the Hamburg Center for Translational Immunology (HCTI) and the Center for Biomedical AI (bAIome).<>He studied biology and molecular biosciences at Heidelberg University and obtained his doctorate in 2017 for research in systems biology at Heidelberg University and the German Cancer Research Center (DKFZ). He subsequently worked as a postdoctoral fellow in the laboratories of Ido Amit and Eran Elinav at the Weizmann Institute of Science in Israel.<>His research combines experimental immunology, single-cell and multi-omics measurements, and mechanistic mathematical modelling to study the interplay between metabolism, immunity and tissue homeostasis. His group investigates these interactions in adipose tissue, the liver and the intestine, with a particular interest in understanding inflammatory disease and cancer, and developing testable predictions from complex biological data.

Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Scientific discovery seeks mechanisms that explain observations and predict beyond the measurements that produced them. Whereas large language models (LLMs) encode knowledge implicitly, mechanistic world models organise it as a parsimonious set of explicit, modular, reusable mechanisms whose predictions can be scored against data. In biology, where measurements are sparse and noisy, mechanistic world modelling must discover latent states and governing equations jointly, yet the prior knowledge that could constrain this search is largely unstructured. We introduce gemot, an agentic framework for mechanistic world modelling, and evaluate it on 18 published biological problems spanning molecular biology, epidemiology, and immune-cell differentiation, with up to 1,755 training measurements, 65 observables, 170 experimental conditions, and 3 data modalities per problem. gemot is auditable by construction: a semantic layer records each hypothesis, and a Model Context Protocol layer separates hypothesis formulation from numerical evaluation, so hypothesis scoring cannot be fabricated. On every problem, autonomously constructed models match or exceed the reference models in fit and parsimony, and generalise where held-outs exist. We demonstratethat gemot can formulate novel, biologically plausible mechanistic hypotheses. This work will enable decoding of interventional biological data into competing mechanistic hypotheses and design experiments that distinguish between them, and may serve as a blueprint for mechanistic world modelling in other domains.

Referent:  Dr. Fabian Fröhlich

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Fabian is a group leader at the Francis Crick Institute. Before joining the Crick, Fabian was a Human Frontier Science Program Fellow in the Laboratory of Systems Pharmacology at Harvard Medical School, working under the guidance of Peter Sorger. He received his PhD from Technische Universität München, working with Jan Hasenauer and Fabian Theis at the Institute of Computational Biology at Helmholtz Munich.

Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Referent:  PD Dr. Clemens Scherer

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Referent:  Dr.-Ing. Lea C. Brandl

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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